Computer Applications in Sustainable Forest Management by Guofan Shao, Keith M. Reynolds

By Guofan Shao, Keith M. Reynolds

This publication is the main finished and updated remedy of machine functions in forestry. it's the first textual content on software program for woodland administration to stress integration of computing device functions. It additionally deals vital new insights on how one can proceed advancing computational applied sciences in woodland management.

The authors are internationally-recognized experts within the topics presented.

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Extra info for Computer Applications in Sustainable Forest Management Including Perspectives on Collaboration and I

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Images with trees of varying canopy size, a common feature of naturally regenerating mature forests, are a particular challenge for tree identification because a single bright blob could represent a branch, a single tree, or a group of trees. On one hand, this complexity is a source of error, because it adds variability to the representation of the tree. g. Brandtberg 1997; 2002, Pollock 1996). However, computer-based tree identification requires high-level algorithms to exploit Brandtberg and Warner 24 such complex spatial information, and may be less accurate than a human interpreter (Pollock, 1996).

Larsen and M. 5 Model-based approach in 3D In concluding their report on template matching, Larsen and Rudemo (1998) discuss how stereo methods might improve their optical model. Gong et al. (2002) describe such a 3D model-based approach using stereo pairs, in which the problem is defined as an optimal tree model determination issue. The individual trees are modeled as hemi-ellipsoids with a total of seven parameters: tree top location in 3D, trunk base height, crown depth, crown radius, and a coefficient for crown curvature of the canopy envelope.

There were only 3 Introduction 5 civil remote sensing satellites in the 1960s and 4 in the 1970s, but the number jumped to 8 in the 1980s and 16 in the 1990s (SCSAC 2000). Since the 1970s, digital remote sensing has renewed and expanded capabilities for data acquisition that are improved with the increases of spatial, spectral, temporal, and radiometric resolutions (Figure 1-2). Active remote sensing, such as RADAR and lidar, provide new means of data acquisition. All these advancements have significant implications to forestry.

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